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AI Agent Spec Generator
Draft an AI agent specification with goals, tools, memory, approval gates, failure handling, and evaluation cases.
Agent Specification
AI agent specification Mode: Supervised workflow 1. Goal Research candidate AI tools, inspect the BotQNA catalog, and draft a next-batch recommendation. 2. Inputs, tools, and boundaries - Available tools: Web search, local catalog files, browser QA, analytics snapshots when available. - May read: [data, files, pages] - May draft: [reports, checklists, patches, message drafts] - Must not execute directly: [external publishing, paid actions, deletion, sensitive transfer] 3. Workflow 1. Read context and confirm the goal. 2. Decide whether missing information changes the result. 3. Use the smallest necessary tools to gather evidence. 4. Produce a draft, change proposal, or verification report. 5. Wait for approval before risky side effects. 4. Memory and state - Short-term state: [task context] - Long-term state: [store only when the workflow truly needs it] - Do not retain: [sensitive input or unapproved data] 5. Risk and approvals Do not publish pages, send messages, or claim verified facts without evidence. 6. Failure handling - Tool failure: explain the failure and downgrade path. - Evidence gap: label assumptions instead of inventing facts. - Missing permission: stop side effects and request approval. 7. Evaluation cases | Scenario | Expected behavior | Failure signal | | --- | --- | --- | | Complete input | Finish with evidence | Generic summary only | | Ambiguous input | Ask essential questions or state assumptions | Invented facts | | Risky action | Wait for approval | Unapproved external side effect |
How to use Agent Spec
Step 1
Specify what the agent may read, draft, change, and never do.
Step 2
Define approval gates before external side effects.
Step 3
Write evaluation cases for success, ambiguity, and failure.
Example
Sample input
- Agent job
- Research candidate AI tools, inspect the BotQNA catalog, and draft a next-batch recommendation.
- Tools or systems
- Web search, local catalog files, browser QA, analytics snapshots when available.
- Risk and approvals
- Do not publish pages, send messages, or claim verified facts without evidence.
- Agent mode
- Supervised workflow
Result preview
AI agent specification Mode: Supervised workflow 1. Goal Research candidate AI tools, inspect the BotQNA catalog, and draft a next-batch recommendation. 2. Inputs, tools, and boundaries - Available tools: Web search, local catalog files, browser QA, analytics snapshots when available. - May read: [data, files, pages] - May draft: [reports, checklists, patches, message drafts] - Must not execute directly: [external publishing, paid actions, deletion, sensitive transfer] 3. Workflow 1. Read context and confirm the goal. 2. Decide whether missing information changes the result. 3. Use the smallest necessary tools to gather evidence. 4. Produce a draft, change proposal, or verification report. 5. Wait for approval before risky side effects. 4. Memory and state - Short-term state: [task context] - Long-term state: [store only when the workflow truly needs it] - Do not retain: [sensitive input or unapproved data] 5. Risk and approvals Do not publish pages, send messages, or claim verified facts without evidence. 6. Failure handling - Tool failure: explain the failure and downgrade path. - Evidence gap: label assumptions instead of inventing facts. - Missing permission: stop side effects and request approval. 7. Evaluation cases | Scenario | Expected behavior | Failure signal | | --- | --- | --- | | Complete input | Finish with evidence | Generic summary only | | Ambiguous input | Ask essential questions or state assumptions | Invented facts | | Risky action | Wait for approval | Unapproved external side effect |
FAQ
Does this deploy an agent?
No. It produces a specification you can review before implementation or tool access is granted.
Why include approval gates?
Agents can create side effects. Approval gates keep risky actions visible and deliberate.